There are many contexts in which an investigatory process may be used to discover facts and information. For example, in the context of product manufacturing, an investigation may be conducted to determine the root cause of a manufacturing defect. As another example, an investigation in the context of criminal justice may be conducted to identify the perpetrator of a crime. Broadly speaking, an investigatory process generally involves gathering data, analyzing the data, and drawing conclusions from the analysis of the data. Given the wealth of readily available computerized information in the digital information age of today, the data gathering step of the investigatory process may be technically challenging because pertinent data may be spread out among many disparate computer systems and may be maintained in many different data types and formats. The vast amount of data in combination with the varied data types and formats gives rise to an additional challenge in the analysis step of the investigatory process given the difficulty in identifying a most relevant subset of the pertinent data and identifying critical connections between data points needed to produce accurate and informed conclusions.
Furthermore, although a prior investigation may have led to conclusions that may be useful in future investigations, conventional computerized systems used to facilitate investigations typically fail to provide a mechanism whereby investigators may leverage knowledge or solutions gleaned in previous related investigations. As a result, the entire investigation process must be repeated despite prior efforts made in related investigation that may otherwise be used to mitigate computational and workflow inefficiencies and reduce the use of computational and network resources.